Exercise 2

 

These exercises are about manipulate single-cell data with Seurat. Please download the counting matrix from BOX and loading them into a Seurat object. Or you may also used the rds file data/scSeq_CKO_sceSub.rds.

If you want to load all dataset

library(DropletUtils)
library(DropletTestFiles)
fname <- "~path to the 10X counting matrix"
sce <- read10xCounts(fname, col.names=TRUE)
saveRDS(sce,"path to rds file")

If you want to load a subset

library(DropletUtils)
library(DropletTestFiles)
sce <- readRDS("data/scSeq_CKO_sceSub.rds")

Exercise 2 - Data manipulation with Bioconductor packages

Identify empty droplets

1. Please draw knee plot and identify inflection point and knee point.

## Warning in xy.coords(x, y, xlabel, ylabel, log): 1 y value <= 0 omitted from
## logarithmic plot

2. Please identify non-empty droplet and compare to the results with hard cut-off

• set limit as “100” (filter out dorplets with UMI counts less than 100)
• FDR cut-off for non-empty droplet is 0.001

## DataFrame with 400000 rows and 5 columns
##                        Total   LogProb    PValue   Limited       FDR
##                    <integer> <numeric> <numeric> <logical> <numeric>
## CCACGGAAGCTCTCGG-1         0        NA        NA        NA        NA
## TGTGGTAAGAGTCGGT-1         2  -11.1237 0.7561244     FALSE   0.99174
## CCCTCCTTCGTCTGCT-1         2  -18.2572 0.0727927     FALSE   0.66022
## ACTGAGTGTGTCGCTG-1         0        NA        NA        NA        NA
## CGTAGCGTCTCTGCTG-1         0        NA        NA        NA        NA
## ...                      ...       ...       ...       ...       ...
## AGAGCTTGTCCGACGT-1         0        NA        NA        NA        NA
## GCTGCGACAATAGCAA-1         0        NA        NA        NA        NA
## CGAACATAGTGAAGTT-1         2  -12.3017  0.632437     FALSE   0.99174
## TAGCCGGCATGTCGAT-1         0        NA        NA        NA        NA
## CGAGAAGAGCAGATCG-1         0        NA        NA        NA        NA

##    Mode   FALSE    TRUE    NA's 
## logical  211696    2110  186194

##        Limited
## Sig      FALSE   TRUE
##   FALSE 209919   1777
##   TRUE       1   2109

data normalization and clustering

3. Please mak data normalization and clustering

• Normalize and log transform the counts
• Modeling and identify top variable features
• Make UMAP plot
• graphic based clustering

Evaluate ambient RNA contamination

4. Please esitmate ambient RNA contamination, remove them, and test by using Hba-a1 gene

## ENSMUSG00000051951 ENSMUSG00000025902 ENSMUSG00000033845 ENSMUSG00000025903 
##       1.669575e-07       1.669575e-07       1.407972e-04       5.201547e-05 
## ENSMUSG00000104217 ENSMUSG00000033813 
##       1.669575e-07       6.783772e-05

5. Please re-clustering after ambient RNA removal

Remove doublets

6. Please estimate doublets and evaluate the doublets were enriched in any clusters or not.Then try to remove the doublet cells/cclusters.

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0422  0.3840  0.7385  0.9630  1.3082  7.2457

## 
##   no  yes 
## 2004  106

Advanced QC plots

7. Please estimate mitochondrial contents (is.mito), read counts (sum) and gene counts (detected) for each cell. Then, draw plots.

• violin plots of is.mito, sum, and detected
• sccater plots for is.mito vs sum and detected vs sum

8. Estimate variance explaination and try to figure out which factor is in the majority of variance.

## Warning: Removed 2785 rows containing non-finite values (stat_density).